Advancements in computing devices and image analysis techniques have led to a variety of innovations in identifying digital images that are visually similar. For example, image analysis systems are now able to analyze high-resolution digital images to identify objects within the images and search through terabytes of information stored in digital image databases to identify other digital images that depict the same or similar objects.
Despite these advances however, conventional image analysis systems continue to suffer from a number of disadvantages, particularly in the accuracy and flexibility of identifying similar digital images. For instance, while conventional image analysis systems can identify the same objects in two different digital images, these systems often disregard other aspects of the images (e.g., backgrounds, spatial arrangement of objects, and other visual attributes of the images). Indeed, because conventional image analysis systems often rely solely on semantic content to classify images based on various image tags, these systems are too object-focused in their analysis. As a result, conventional image analysis systems often produce inaccurate results when determining the visual similarity of two images. This is a particularly significant problem because, due to this inaccuracy, users of conventional image analysis systems are often required to spend an inordinate amount of time performing excessive user actions searching through match results before locating desirable image matches.
In addition, conventional image analysis systems are often inflexible. Indeed, as mentioned, conventional image analysis systems are often one-dimensional in that they only match digital images based on identifying particular objects within the images. Many of these systems also require very specific input (e.g., a single digital image) to use as basis for finding matching images. In cases where a user wants to find similar images for more than one input image, many conventional image analysis systems require performing multiple single-image searches and/or retraining an analysis model to accommodate multiple input images. As a result of their inflexible nature, these conventional systems are often incapable of tailoring image matching to the needs of a user beyond searching individual images for particular objects.
Thus, there are several disadvantages with regard to conventional image analysis systems.